A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes

Hepatitis B virus (HBV) infection can lead to hepatocellular carcinoma, and HBV integration into the host genome is regularly observed in the liver of chronic HBV carriers and is speculated to trigger carcinogenesis. To detect HBV integration, PCR-based methods are sensitive but may not be effective depending on the HBV integration site. High-throughput sequencing reveals not only HBV integration but also the site. However, it is still expensive for clinical applications. In situ hybridization is efficient and allows detection of DNA and RNA in single cells and has been applied to study HBV infection and dynamics. The technique, however, has not been applied to detect HBV integration. The task is challenging because of the small copy number of integrated HBV genomes, which results in a small signal-to-noise ratio. Here, we developed a fluorescence in situ hybridization approach for examining HBV integration in each liver cell. The obtained images of cells were analyzed using a deep learning model. Using several hepatoma cell lines with and without integrated HBV DNA, we showed that our trained neural network achieved an over 90% accuracy for majority of positive and negative control cells. This is the first proof-of-concept study showing that fluorescence imaging and deep learning are useful in detecting HBV integration at the single-cell level.